Multi-View Three-Dimensional Point Cloud Reconstruction

The present invention relates to artificial intelligence, image analysis, generation of three-dimensional images, computer vision, virtual reality, augmented reality, and/or autonomous driving.

One example embodiment provides a computer-implemented method that may include one or more of executing a neural network on an image of a scene to generate a description of the scene, generating a prompt that includes the description of the scene and a request to generate multiple views of the scene, executing a machine learning model on the prompt to generate multiple descriptions of the multiple views of the scene, respectively, the machine learning model having been trained to perform one or more generative tasks, and executing a transformer model on the multiple descriptions of the multiple views of the scene and the image of the scene to generate a three-dimensional visual representation of the scene in virtual space.

Another example embodiment provides a computer system that may include a processor set, a set of one or more computer-readable storage media, and program instructions, collectively stored in the set of one or more storage media, that cause the processor set to perform computer operations that may include one or more of execute a neural network on an image of a scene to generate a description of the scene, generate a prompt that includes the description of the scene and a request to generate multiple views of the scene, execute a machine learning model on the prompt to generate multiple descriptions of the multiple views of the scene, respectively, the machine learning model having been trained to perform one or more generative tasks, and execute a transformer model on the multiple descriptions of the multiple views of the scene and the image of the scene to generate a three-dimensional visual representation of the scene in virtual space.

A further example embodiment provides a computer program product that may include a set of one or more computer-readable storage media, and program instructions, collectively stored in the set of one or more computer-readable storage media, for causing a processor set to perform computer operations that may include one of more of executing a neural network on an image of a scene to generate a description of the scene, generating a prompt that includes the description of the scene and a request to generate multiple views of the scene, executing a machine learning model on the prompt to generate multiple descriptions of the multiple views of the scene, respectively, the machine learning model having been trained to perform one or more generative tasks, and executing a transformer model on the multiple descriptions of the multiple views of the scene and the image of the scene to generate a three-dimensional visual representation of the scene in virtual space.

It is to be understood that although this disclosure includes a detailed description of cloud computing, implementation of the teachings recited herein is not limited to a cloud computing environment. Rather, embodiments of the instant solution are capable of being implemented in conjunction with any other type of computing environment now known or later developed.

According to an aspect of the example embodiments, there is provided a computer-implemented method that includes executing a neural network on an image of a scene to generate a description of the scene, generating a prompt that includes the description of the scene and a request to generate multiple views of the scene, executing a machine learning model on the prompt to generate multiple descriptions of the multiple views of the scene, respectively, the machine learning model having been trained to perform one or more generative tasks, and executing a transformer model on the multiple descriptions of the multiple views of the scene and the image of the scene to generate a three-dimensional visual representation of the scene in virtual space. A technical advantage of the apparatus is that a more accurate a three-dimensional point cloud representation of an image can be generated in comparison to traditional point cloud generation systems because semantic information about a scene is considered along with the image data thereby enabling a better visual understanding of the scene.

In some embodiments, the neural network may include an image captioning model, and the executing the neural network may include executing the image captioning model on the image of the scene to generate a semantic description of spatial relationships between objects in the scene. The technical effect of this feature is generating a semantic description from an image for use in generating a 3D point cloud representation of the image.

In some embodiments, the neural network may include a contrastive learning model, and the executing the neural network may include connecting the image of the scene to text an embedding space and generating the description of the scene based on the text. The technical effect of this feature is using a machine learning model to identify text that is similar to images based on a shared embedding space for the text and the images.

In some embodiments, generating the prompt may include inserting the description of the scene into a prompt template to generate the prompt, where the prompt template may include a request to generate descriptions of different views of the scene from different perspectives. The technical advantage of this feature is using prompt engineering to instruct a large language model to generate rich semantic information about a scene that can be used for enhancing 3D point cloud reconstruction of the scene.

In some embodiments, the executing the machine learning model on the prompt may include executing the machine learning model on the prompt to generate descriptions of some or all of a group consisting of a view from a top of the scene, a view from a bottom of the scene, a view from left of the scene, and a view from right of the scene. The technical effect of this feature is creating different descriptions of different views of the same scene which provide rich semantic information to be used during 3D point cloud reconstruction of the scene.

In some embodiments, executing the transformer model may include executing a two-dimensional view encoder on the multiple descriptions of the multiple views of the scene and the image of the scene to generate encodings. The technical effect of this feature is converting both image data and semantic text data into the same format (vector encodings) such that both the image data and the text data can be fused together.

In some embodiments, executing the transformer model may further include executing a three-dimensional volume decoder on the encodings to generate the three-dimensional visual representation of the scene in virtual space. The technical benefit of this feature is that both the image data and the semantic data are fused together and used to generate the three-dimensional representation of the scene, rather than just image data. The result is a more accurate three-dimensional representation of the scene.

According to an aspect of the example embodiments, there is provided a computer system that includes a processor set, a set of one or more computer-readable storage media, and program instructions, collectively stored in the set of one or more storage media, for causing the processor set to perform operations that include executing a neural network on an image of a scene to generate a description of the scene, generating a prompt that includes the description of the scene and a request to generate multiple views of the scene, executing a machine learning model on the prompt to generate multiple descriptions of the multiple views of the scene, respectively, the machine learning model having been trained to perform one or more generative tasks, and executing a transformer model on the multiple descriptions of the multiple views of the scene and the image of the scene to generate a three-dimensional visual representation of the scene in virtual space. A technical advantage of the computer-implemented method is that a more accurate a three-dimensional point cloud representation of an image can be generated in comparison to traditional point cloud generation systems because semantic information about a scene is considered along with the image data thereby enabling a better visual understanding of the scene.

In some embodiments for the computer operations caused to be performed by the processor set executing the program instructions of the computer system, the neural network may include an image captioning model, and the executing the neural network may include executing the image captioning model on the image of the scene to generate a semantic description of spatial relationships between objects in the scene. The technical effect of this feature is generating a semantic description from an image for use in generating a 3D point cloud representation of the image.

In some embodiments for the computer operations caused to be performed by the processor set executing the program instructions of the computer system, the neural network may include a contrastive learning model, and the executing the neural network may include connecting the image of the scene to text an embedding space and generating the description of the scene based on the text. The technical effect of this feature is using a machine learning model to identify text that is similar to images based on a shared embedding space for the text and the images.

In some embodiments for the computer operations caused to be performed by the processor set executing the program instructions of the computer system, generating the prompt may include inserting the description of the scene into a prompt template to generate the prompt, where the prompt template may include a request to generate descriptions of different views of the scene from different perspectives. The technical advantage of this feature is using prompt engineering to instruct a large language model to generate rich semantic information about a scene that can be used for enhancing 3D point cloud reconstruction of the scene.

In some embodiments for the computer operations caused to be performed by the processor set executing the program instructions of the computer system, the executing the machine learning model on the prompt may include executing the machine learning model on the prompt to generate descriptions of some or all of a group consisting of a view from a top of the scene, a view from a bottom of the scene, a view from left of the scene, and a view from right of the scene. The technical effect of this feature is creating different descriptions of different views of the same scene which provide rich semantic information to be used during 3D point cloud reconstruction of the scene.

In some embodiments for the computer operations caused to be performed by the processor set executing the program instructions of the computer system, executing the transformer model may include executing a two-dimensional view encoder on the multiple descriptions of the multiple views of the scene and the image of the scene to generate encodings. The technical effect of this feature is converting both image data and semantic text data into the same format (vector encodings) such that both the image data and the text data can be fused together.

In some embodiments for the computer operations caused to be performed by the processor set executing the program instructions of the computer system, executing the transformer model may further include executing a three-dimensional volume decoder on the encodings to generate the three-dimensional visual representation of the scene in virtual space. The technical benefit of this feature is that both the image data and the semantic data are fused together and used to generate the three-dimensional representation of the scene, rather than just image data. The result is a more accurate three-dimensional representation of the scene.

According to an aspect of the example embodiments, there is provided a computer program product that includes a set of one or more computer-readable storage media, and program instructions, collectively stored in the set of one or more computer-readable storage media, for causing a processor set to perform computer operations that include executing a neural network on an image of a scene to generate a description of the scene, generating a prompt that includes the description of the scene and a request to generate multiple views of the scene, executing a machine learning model on the prompt to generate multiple descriptions of the multiple views of the scene, respectively, the machine learning model having been trained to perform one or more generative tasks, and executing a transformer model on the multiple descriptions of the multiple views of the scene and the image of the scene to generate a three-dimensional visual representation of the scene in virtual space. A technical advantage of the computer program product is that a more accurate a three-dimensional point cloud representation of an image can be generated in comparison to traditional point cloud generation systems because semantic information about a scene is considered along with the image data thereby enabling a better visual understanding of the scene.

In some embodiments for the computer operations caused to be performed by the processor set executing the program instructions of the computer program product, the neural network may include an image captioning model, and the executing the neural network may include executing the image captioning model on the image of the scene to generate a semantic description of spatial relationships between objects in the scene. The technical effect of this feature is generating a semantic description from an image for use in generating a 3D point cloud representation of the image.

In some embodiments for the computer operations caused to be performed by the processor set executing the program instructions of the computer program product, the neural network may include a contrastive learning model, and the executing the neural network may include connecting the image of the scene to text an embedding space and generating the description of the scene based on the text. The technical effect of this feature is using a machine learning model to identify text that is similar to images based on a shared embedding space for the text and the images.

In some embodiments for the computer operations caused to be performed by the processor set executing the program instructions of the computer program product, generating the prompt may include inserting the description of the scene into a prompt template to generate the prompt, where the prompt template may include a request to generate descriptions of different views of the scene from different perspectives. The technical advantage of this feature is using prompt engineering to instruct a large language model to generate rich semantic information about a scene that can be used for enhancing 3D point cloud reconstruction of the scene.

In some embodiments for the computer operations caused to be performed by the processor set executing the program instructions of the computer program product, the executing the machine learning model on the prompt may include executing the machine learning model on the prompt to generate descriptions of some or all of a group consisting of a view from a top of the scene, a view from a bottom of the scene, a view from left of the scene, and a view from right of the scene. The technical effect of this feature is creating different descriptions of different views of the same scene which provide rich semantic information to be used during 3D point cloud reconstruction of the scene.

In some embodiments for the computer operations caused to be performed by the processor set executing the program instructions of the computer program product, executing the transformer model may include executing a two-dimensional view encoder on the multiple descriptions of the multiple views of the scene and the image of the scene to generate encodings. The technical effect of this feature is converting both image data and semantic text data into the same format (vector encodings) such that both the image data and the text data can be fused together.

In some embodiments for the computer operations caused to be performed by the processor set executing the program instructions of the computer program product, executing the transformer model may further include executing a three-dimensional volume decoder on the encodings to generate the three-dimensional visual representation of the scene in virtual space. The technical benefit of this feature is that both the image data and the semantic data are fused together and used to generate the three-dimensional representation of the scene, rather than just image data. The result is a more accurate three-dimensional representation of the scene.

The system described herein may be hosted within a software application, a service, or the like, which may be hosted by a host platform such as a cloud platform, a web server, a database, or the like.

Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.

On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider. Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or data center). Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. Characteristics are as follows:

Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure, including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer can deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). Service Models are as follows:

Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community with shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by organizations or a third party and may exist on-premises or off-premises. Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). Deployment Models are as follows:

A cloud computing environment is service-oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.

The instant features, structures, or characteristics as described throughout this specification may be combined or removed in any suitable manner in one or more embodiments. For example, the usage of the phrases “example embodiments,” “some embodiments,” or other similar language, throughout this specification refers to the fact that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment. Thus, appearances of the phrases “example embodiments,” “in some embodiments,” “in other embodiments,” or other similar language, throughout this specification do not necessarily all refer to the same group of embodiments, and the described features, structures, or characteristics may be combined or removed in any suitable manner in one or more embodiments. Further, in the diagrams, any connection between elements can permit one-way and/or two-way communication even if the depicted connection is a one-way or two-way arrow. Also, any device depicted in the drawings can be a different device. For example, if a mobile device is shown sending information, a wired device could also be used to send the information.

The example embodiments are directed to an innovative system for generating 3D representations of an image, also referred to as 3D point cloud reconstruction. The system leverages multiple artificial intelligence/machine learning models (e.g., an ensemble, etc.) including a large language model that is configured to extract semantic features (textual descriptions, etc.) from the raw image data and integrate it into existing 3D reconstruction neural networks. For example, the image data may be processed using a pre-trained large language model that can obtain semantic representations of the images, which include higher-level semantic information such as object categories, scene descriptions, and contextual relationships.

The semantic features can be integrated into feature extraction and representation stages of a 3D reconstruction neural network. According to various embodiments, by combining geometric information from a scene with semantic information from the scene, the system can achieve a more comprehensive and accurate 3D point cloud reconstruction. This is because the combination of text and images can better capture the details and semantic correlations present in the images. The system fuses together both the image data and the semantic text data using a combination of embedding models and neural networks. The use of large language models from the natural language processing domain with the task of 3D reconstruction provides new insights and methods for the image-to-3D point cloud reconstruction process.

The direct use of a single image for 3D reconstruction often leads to unsatisfactory results due to the limited amount of information available from the image. However, even a single image typically contains rich textual content that can vividly describe the color, detailed features, and spatial relationships between objects. Additionally, leveraging the information from existing images and combining it with a current generative language model allows for the expansion of associations with the image content from different perspectives, enriching the scene information. The example embodiments describe a system that can extract this semantic data from the image and use to during a 3D reconstruction process which incorporates the semantic data.

Some of the benefits of the example embodiments including combining large language models in the field of natural language processing with 3D reconstruction tasks to generate more accurate 3D point cloud reconstructions (in virtual space) of raw image data than compared to traditional 3D construction processes which do not rely on semantic information. This is because, in comparison to traditional 3D reconstruction systems, the introduction of a large language model which can extract richer semantic features from images, can significantly improve the accuracy of reconstruction and the ability to restore details.

1 FIG. 100 illustrates a computing environmentaccording to an embodiment of the instant solution. Various aspects of the present disclosure are described by narrative text, flowcharts, block diagrams of computer systems and/or block diagrams of the machine logic included in computer program product (CPP) embodiments. With respect to any flowcharts, depending upon the technology involved, the operations can be performed in a different order than what is shown in a given flowchart. For example, again, depending upon the technology involved, two operations shown in successive flowchart blocks may be performed in reverse order, as a single integrated step, concurrently, or in a manner at least partially overlapping in time.

A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim. A “storage device” is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, the computer readable storage medium may be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the foregoing. Some known types of storage devices that include these mediums include: diskette, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded device (such as punch cards or pits/lands formed in a major surface of a disc) or any suitable combination of the foregoing. A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media. As will be understood by those of skill in the art, data is typically moved at some occasional points in time during normal operations of a storage device, such as during access, de-fragmentation or garbage collection, but this does not render the storage device as transitory because the data is not transitory while it is stored.

1 FIG. 100 116 116 100 101 102 103 104 105 106 101 110 120 121 111 112 113 122 116 114 123 124 125 115 104 130 105 140 141 142 143 144 Referring to, computing environmentcontains an example of an environment for executing at least some of the computer code involved in performing the inventive methods, such as multi-view 3D point cloud reconstruction system. In addition to block, computing environmentincludes, for example, computer, wide area network (WAN), end-user device (EUD), remote server, public cloud, and private cloud. In this embodiment, computerincludes processor set(including processing circuitryand cache), communication fabric, volatile memory, persistent storage(including operating systemand block, as identified above), peripheral device set(including user interface (UI), device set, storage, and Internet of Things (IoT) sensor set), and network module. Remote serverincludes remote database. Public cloudincludes gateway, cloud orchestration module, host physical machine set, virtual machine set, and container set.

101 130 100 101 101 101 1 FIG. COMPUTERmay take the form of a desktop computer, laptop computer, tablet computer, smartphone, smartwatch or other wearable computer, mainframe computer, quantum computer or any other form of computer or mobile device now known or to be developed in the future that is capable of running a program, accessing a network or querying a database, such as remote database. As is well understood in the art of computer technology, and depending upon the technology, the performance of a computer-implemented method may be distributed among multiple computers and/or between multiple locations. On the other hand, in this presentation of the computing environment, a detailed discussion is focused on a single computer, specifically the computer, to keep the presentation as simple as possible. Computermay be located in a cloud, even though it is not shown in a cloud in. On the other hand, computeris not required to be in a cloud except to any extent as may be affirmatively indicated.

110 120 120 121 110 110 PROCESSOR SETincludes one, or more, computer processors of any type now known or to be developed in the future. Processing circuitrymay be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. Processing circuitrymay implement multiple processor threads and/or multiple processor cores. Cacheis a memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on processor set. Cache memories are typically organized into multiple levels depending upon relative proximity to the processing circuitry. Alternatively, some, or all, of the cache for the processor set may be located “off-chip.” In some computing environments, processor setmay be designed for working with qubits and performing quantum computing.

101 110 101 121 110 100 116 113 Computer readable program instructions are typically loaded onto computerto cause a series of operational steps to be performed by processor setof computerand thereby effect a computer-implemented method, such that the instructions thus executed will instantiate the methods specified in flowcharts and/or narrative descriptions of computer-implemented methods included in this document (collectively referred to as “the inventive methods”). These computer readable program instructions are stored in various types of computer readable storage media, such as cacheand the other storage media discussed below. The program instructions, and associated data, are accessed by processor setto control and direct performance of the inventive methods. In computing environment, at least some of the instructions for performing the inventive methods may be stored in blockin persistent storage.

111 101 COMMUNICATION FABRICis the signal conduction path that allows the various components of computerto communicate with each other. Typically, this fabric comprises switches and electrically conductive paths, such as the switches and electrically conductive paths that make up buses, bridges, physical input/output ports, and the like. Other types of signal communication paths may be used, such as fiber optic communication paths and/or wireless communication paths.

112 101 112 101 101 VOLATILE MEMORYis any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM. Typically, the volatile memory is characterized by random access, but this is not required unless affirmatively indicated. In computer, the volatile memoryis located in a single package and is internal to computer, but, alternatively or additionally, the volatile memory may be distributed over multiple packages and/or located externally with respect to computer.

113 101 113 113 122 116 PERSISTENT STORAGEis any form of non-volatile storage for computers that is now known or to be developed in the future. The non-volatility of this storage means that the stored data is maintained regardless of whether power is being supplied to computerand/or directly to persistent storage. Persistent storagemay be a read-only memory (ROM), but typically at least a portion of the persistent storage allows writing of data, deletion of data, and re-writing of data. Some familiar forms of persistent storage include magnetic disks and solid-state storage devices. Operating systemmay take several forms, such as various known proprietary operating systems or open-source Portable Operating System Interface type operating systems that employ a kernel. The code included in blocktypically includes at least some of the computer code involved in performing the inventive methods.

114 101 101 123 124 124 124 101 101 125 PERIPHERAL DEVICE SETincludes the set of peripheral devices of computer. Data communication connections between the peripheral devices and the other components of computermay be implemented in various ways, such as Bluetooth® connections, Near-Field Communication (NFC) connections, connections made by cables (such as universal serial bus (USB) type cables), insertion type connections (for example, secure digital (SD) card), connections made through local area communication networks and even connections made through wide area networks such as the internet. In various embodiments, UI device setmay include components such as a display screen, speaker, microphone, wearable devices (such as goggles and smartwatches), keyboard, mouse, printer, touchpad, game controllers, and haptic devices. Storageis external storage, such as an external hard drive, or insertable storage, such as an SD card. Storagemay be persistent and/or volatile. In some embodiments, storagemay take the form of a quantum computing storage device for storing data in the form of qubits. In embodiments where computeris required to have a large amount of storage (for example, where computerlocally stores and manages a large database) then this storage may be provided by peripheral storage devices designed for storing very large amounts of data, such as a storage area network (SAN) that is shared by multiple, geographically distributed computers. IoT sensor setis made up of sensors that can be used in Internet of Things applications. For example, one sensor may be a thermometer, and another sensor may be a motion detector.

115 101 102 115 115 115 101 115 NETWORK MODULEis the collection of computer software, hardware, and firmware that allows computerto communicate with other computers through WAN. Network modulemay include hardware, such as modems or Wi-Fi® signal transceivers, software for packetizing and/or de-packetizing data for communication network transmission, and/or web browser software for communicating data over the internet. In some embodiments, network control functions and network forwarding functions of network moduleare performed on the same physical hardware device. In other embodiments (for example, embodiments that utilize software-defined networking (SDN)), the control functions and the forwarding functions of network moduleare performed on physically separate devices, such that the control functions manage several different network hardware devices. Computer readable program instructions for performing the inventive methods can typically be downloaded to computerfrom an external computer or external storage device through a network adapter card or network interface included in network module.

102 WANis any wide area network (for example, the internet) capable of communicating computer data over non-local distances by any technology for communicating computer data now known or to be developed in the future. In some embodiments, the WAN may be replaced and/or supplemented by local area networks (LANs) designed to communicate data between devices located in a local area, such as a Wi-Fi® network. The WAN and/or LANs typically include computer hardware such as copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and edge servers.

103 101 101 103 101 101 115 101 102 103 103 103 END USER DEVICE (EUD)is any computer system that is used and controlled by an end user (for example, a customer of an enterprise that operates computer) and may take any of the forms discussed above in connection with computer. EUDtypically receives helpful and useful data from the operations of computer. For example, in a hypothetical case where computeris designed to provide a recommendation to an end user, this recommendation would typically be communicated from network moduleof computerthrough WANto EUD. In this way, EUDcan display, or otherwise present, the recommendation to an end user. In some embodiments, EUDmay be a client device, such as thin client, heavy client, mainframe computer, desktop computer, and so on.

104 101 104 101 104 101 101 101 130 104 REMOTE SERVERis any computer system that serves at least some data and/or functionality to computer. Remote servermay be controlled and used by the same entity that operates computer. Remote serverrepresents the machine(s) that collect and store helpful and useful data for use by other computers, such as computer. For example, in a hypothetical case where computeris designed and programmed to provide a recommendation based on historical data, this data may be provided to computerfrom remote databaseof remote server.

105 105 141 105 142 105 143 144 141 140 105 102 PUBLIC CLOUDis any computer system available for use by multiple entities that provides on-demand availability of computer system resources and/or other computer capabilities, especially data storage (cloud storage) and computing power, without direct active management by the user. Cloud computing typically leverages sharing of resources to achieve coherence and economies of scale. The direct and active management of the computing resources of public cloudis performed by the computer hardware and/or software of cloud orchestration module. The computing resources provided by public cloudare typically implemented by virtual computing environments that run on various computers making up the computers of host physical machine set, which is the universe of physical computers in and/or available to public cloud. The virtual computing environments (VCEs) typically take the form of virtual machines from virtual machine setand/or containers from container set. It is understood that these VCEs may be stored as images and may be transferred among and between the various physical machine hosts, either as images or after instantiation of the VCE. Cloud orchestration modulemanages the transfer and storage of images, deploys new instantiations of VCEs and manages active instantiations of VCE deployments. Gatewayis the collection of computer software, hardware, and firmware that allows public cloudto communicate through WAN.

Some further explanations of virtualized computing environments (VCEs) will now be provided. VCEs can be stored as “images.” A new active instance of the VCE can be instantiated from the image. Two familiar types of VCEs are virtual machines and containers. A container is a VCE that uses operating-system-level virtualization. This refers to an operating system feature in which the kernel allows the existence of multiple isolated user-space instances, called containers. These isolated user-space instances typically behave as real computers from the point of view of programs running in them. A computer program running on an ordinary operating system can utilize all resources of that computer, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running inside a container can only use the contents of the container and devices assigned to the container, a feature which is known as containerization.

106 105 106 102 105 106 PRIVATE CLOUDis similar to public cloud, except that the computing resources are only available for use by a single enterprise. While private cloudis depicted as communicating with WAN, in other embodiments, a private cloud may be disconnected from the internet entirely and only accessible through a local/private network. A hybrid cloud is a composition of multiple clouds of different types (for example, private, community, or public cloud types), often respectively implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardized or proprietary technology that enables orchestration, management, and/or data/application portability between the multiple constituent clouds. In this embodiment, public cloudand private cloudare both parts of a larger hybrid cloud.

2 FIG.A 2 FIG.A 200 220 222 202 224 224 224 illustrates a computing environmentA for 3D point cloud generation according to the examples and features of the instant solution. Referring to, a host platformmay host a software applicationwhich may be used for generating a 3D point cloud “reconstruction” of an image of a scene. This generation is based on at least one machine learning (ML) model, e.g., by using at least one ML model. In the examples herein, the at least one ML modelmay include a sequence of models. A script may be used to transfer an output from one model of the sequence of models to an input of another model of the sequence of models in an automated manner.

220 220 222 222 222 220 210 220 2 FIG.A The host platformmay be a cloud platform, a web server, a database, a combination of systems, and the like. The host platformmay provide the software applicationvia an IP address on the World Wide Web. For example, the software applicationmay be a web application resource (WAR) application. As another example, the software applicationmay include a back-end hosted on the host platformand a front-end that is installed within a user device. In the example of, a user may use a user deviceto connect to the host platformover a computer network such as the Internet, a private network, or the like.

210 214 222 212 210 202 214 202 214 202 204 202 214 222 224 202 204 In some embodiments, a user may use the user deviceto view a graphical user interface (GUI)of the software applicationon a display deviceof the user device. In this example, the user may upload the image of the scene, for example, a .JPEG file, a .PNG file, a .TIFF file, a .BMP file, or the like. The image may include raw image data such as an image captured using a camera, or the like. Here, the user may input commands on the GUIto upload the image of the scene. As another example, the user may input commands on the GUIto trigger a conversion of the image of the sceneinto a 3D point cloud reconstruction of the image. For example, the user may press a button and browse to add an image file such as the image of the sceneusing the GUI. The button press may cause the software applicationto execute the at least one ML modelto convert the image of the sceneinto the 3D point cloud reconstruction of the image.

2 FIG.B 2 FIG.A 3 3 FIGS.A-E 200 200 222 224 224 231 233 234 235 231 233 234 235 illustrates a processB of generating a 3D point cloud representation from raw image data according to the examples and features of the instant solution. For example, the processB may be performed by the software applicationshown inusing the at least one ML model. In this example, the at least one ML modelincludes a sequence of ML models including a neural network, a LLM, at least one embedding model, and a 3D volume transformer model. Additional examples of the neural network, the LLM, the at least one embedding model, and the 3D volume transformer modelare further described herein with respect to.

2 FIG.B 202 231 241 241 202 231 241 202 Referring now to, in this example, the image of the sceneis input to a neural networkwhich is configured to generate a description of the scene. The description of the scenemay include a textual description of the content within the raw image data of the image of the scene. In some embodiments, the neural networkis referred to herein as an “image captioning” model because it can create a description of the scenein response to receiving, as input, the image of the scene.

231 231 231 For example, in some embodiments the neural networkhas been trained to identify connections between text and images. For example, the neural networkmay be a BLIP-2 model which stands for “Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models”. The BLIP-2 system leverages frozen pre-trained image encoders and LLMs by training a lightweight multi-layer (e.g., twelve layer) transformer encoder in multi-stage training. In a first training stage, vision-language representation learning is bootstrapped from a frozen image encoder. In a second training stage, vision-to-language generative learning is bootstrapped from a frozen language model. The BLIP-2 model achieves state of the art performance on various vision-language tasks. As another example, the neural networkmay be a Clip model which is a contrastive language model (deep learning model) that combines both images and text into an embedding space enabling the model to identify text and images that are similar (similar embedding locations).

Both Clip and BLIP-2 are vision-language models that work on bridging the gap between visual and textual information. BLIP-2 combines vision and language understanding to generate descriptive text from images and can be used for tasks such as image captioning, visual question answering, and other vision-language tasks. CLIP is a vision-language model that learns to connect images and text in a shared embedding space. CLIP is trained using a contrastive learning approach where it learns to match images with corresponding textual descriptions and distinguish them from unrelated pairs. CLIP can be used for various tasks including zero-shot image classification, where it can classify images based on textual descriptions without additional training. It excels at understanding and generating text descriptions that are semantically aligned with visual content.

232 241 231 242 241 241 241 232 241 231 242 232 231 242 232 241 232 A prompt generatormay receive the output (e.g., the description of the scene) from the neural networkand generate a promptwhich includes a combination of the description of the sceneand a request to generate descriptions of different views of the scenebased on the description of the scene. Here, the prompt generatormay use a template (fixed text) in combination with the description of the scenewhich is dynamically generated by the neural networkto generate the prompt. The template is saved in memory to be used for various inputs and, in some embodiments, the prompt generatoraccesses the stored template to use the stored template with a new output from the neural network(e.g., a new description of a scene) to generate the prompt. Thus, the prompt generatorin at least some embodiments is a software component that executes automatically in response to receiving the description of the scene. A user is able to upload a pre-selected template that is to be used by the prompt generator.

242 233 243 243 243 243 233 241 243 243 243 243 233 242 a b c d a b c d The promptmay be input to the LLMwhich is configured to generate descriptions of different views of the scene,,, and. Here, the LLMmay generate the descriptions as if looking at the scene from different perspectives including a top-down perspective, a bottom-up perspective (upward), a left-to-right perspective, a right-to-left perspective, and/or the like. As a result, the description of the scenemay be converted into the descriptions of different views of the scene,,, and, based on the LLMexecuting on the prompt. The large language model (LLM) may be a pre-trained machine learning model that has been trained to perform natural language understanding and generation, and it may be used to generate diverse textual descriptions from different perspectives.

243 243 243 243 202 234 244 234 a b c d In this example, the descriptions of different views of the scene,,, and, and the image of the scenemay be input to the at least one embedding modeland converted into embeddingsin an embedding space (e.g., vector space). That is, both the descriptions and the image data may each be converted into a common embedding space that is shared by both text and images. In some embodiments, the at least one embedding modelmay include a combination of models/encoders with a first encoder configured to convert the image data to the embedding space and a second encoder configured to convert the descriptions into the embedding space.

244 235 244 204 235 In this example, the embeddingsmay be input to the 3D volume transformer modelwhich converts the embeddingsinto the 3D point cloud reconstruction of the image. An example of the 3D volume transformer modelmay include a 2D-view encoder and a 3D-volume decoder. The 2D-view encoder encodes the relevant information amongst different views via view attention layers. The 3D-volume decoder learns global correlations of different spatial locations in volume attention layers and predicts the final 3D volume output. The system may split the 3D space into a set of tokens and the predicted volumes for each token are finally stitched into the final 3D reconstruction output.

235 235 The 2D-view encoder in the 3D volume transformer modelis responsible for processing and integrating features derived from both image and text embeddings. This integration helps capture a comprehensive representation of the scene from multiple views and perspectives, which is crucial for accurate 3D reconstruction. The 3D volume transformer modeluses self-attention mechanisms within a transformer-based architecture to process concatenated embeddings. This approach allows the model to capture complex dependencies between the visual and textual data. The input consists of concatenated embeddings from the image encoder and the text encoder. The output is a refined multi-view virtual representation of the scene. Here, the final embeddings are used after passing through a series of self-attention layers and normalization steps.

3 FIG.A 3 FIG.A 300 202 231 231 241 241 241 illustrates a processA of generating a semantic description of a scene according to the examples and features of the instant solution. Referring to, the image of the sceneis input to the neural network. In response, the neural networkgenerates the description of the scene. In this example, the description of the sceneis a textual description of the overall scene including a textual description of all of the items in the scene, a textual description of spatial relationships of the items with respect to each other, a textual description of colors, a textual description of locations, and the like. As mentioned previously, the description of the scenemay be generated using a CLIP model, a BLIP-2 model, or the like.

241 231 232 222 2 FIG.A According to various embodiments, the description of the scenemay be output by the neural network, and input to the prompt generator, for example, via a script that is executed by the software applicationshown in.

3 FIG.B 3 FIG.B 300 242 232 241 231 232 310 312 312 314 232 232 312 314 302 241 242 241 312 illustrates a processB of generating a promptaccording to the examples and features of the instant solution. Referring to, the prompt generatormay receive, as input, the description of the scenegenerated by the neural network. In this example, the prompt generatormay query a prompt databasefor a prompt template. The prompt templatemay include an empty slot and a fixed description(static text content) which is the same each time the prompt generatorgenerates a request for a scene regardless of the image content. Here, the prompt generatormay combine the prompt template(including the fixed description) with text contentfrom the description of the sceneto generate the prompt. In some embodiments, the entire text content from the description of the scenemay be used to fill in the empty slot in the prompt template.

3 FIG.C 3 FIG.C 3 FIG.B 2 FIG.A 3 FIG.B 300 243 243 243 243 242 233 222 242 232 233 233 242 314 302 a b c d illustrates a processC of generating the description of different views of a scene,,, and, according to the examples and features of the instant solution. Referring to, the promptgenerated inmay be input to the LLMvia a script that is running through the software applicationshown in the example of. That is, the promptmay be output by the prompt generatorand input to the LLMin an automated manner via an executable script. Here, the LLMmay generate four different descriptions of four different view/perspectives of the same/common scene based on the instructions in the promptwhich includes the fixed descriptionand the text contentshown in.

In this example, the four descriptions that are generated correspond to four different views of the same scene including a top-down view of the scene, a bottom-up view of the scene, a left-to-right view of the scene, and a right-to-left view of the scene. Each of the four descriptions may include overlapping content therein including objects, positions of the objects, locations of other items in the scene such as walls, windows, etc. and the like. In some embodiments, the descriptions may include colors, shading, patterns, and/or the like.

3 FIG.D 3 FIG.D 300 234 320 322 320 202 202 330 322 243 243 243 243 332 324 330 332 334 330 332 334 a b c d illustrates a processD of embedding image content and text content of a scene into a combined embedding according to the examples and features of the instant solution. Referring to, the at least one embedding modelmay include a combination of encoders including an image encoderand a text encoder. Here, the image encodermay receive the image of the sceneand convert the image of the sceneinto a vectorwithin a shared embedding space/vector space. Meanwhile, the text encodermay receive the descriptions of the different views of the scene,,, and, and convert them into a vectorwithin the shared embedding space/vector space. A concatenatormay receive the vectorand the vectorand concatenate the vectors together to generate a concatenated vector(final embedding). The concatenation process may include placing the vectorsandside-by-side to create the concatenated vector. Software libraries integrated within the software application may be used to perform the concatenation process.

3 FIG.E 3 FIG.E 3 FIG.D 300 204 334 234 235 235 340 342 illustrates a processE of generating a 3D point cloud reconstruction of the imagebased on the combined embedding according to the examples of features of the instant solution. Referring to, the concatenated vectoroutput from the at least one embedding modelin, may be input to the 3D volume transformer model. Here, in the depicted embodiment the 3D volume transformer modelincludes a 2D-view encoderand a 3D-view decoder.

340 Here, the 2D-view encodermay be responsible for processing and integrating features derived from both image and text embeddings. This integration helps capture a comprehensive representation of the scene from multiple views and perspectives. It uses self-attention mechanisms within a transformer-based architecture to process the concatenated embeddings. This approach allows the model to capture complex dependencies between the visual and textual data. The input consists of concatenated embeddings from the image encoder and the text encoder. The 3D-volume decoder learns global correlations of different spatial locations in volume attention layers and predicts the final 3D volume output.

204 214 204 204 204 300 2 FIG.A 3 FIG.F The resulting output may include the 3D point cloud reconstruction of the imagewhich may include a virtual representation of the scene and it may be displayed on the GUIthat is shown in the example of. Here, the 3D point cloud reconstruction of the imagemay include a virtual image in virtual space made up of many different points in virtual space. The 3D point cloud reconstructionmay be a virtual reality (VR) image, an augmented reality (AR) image, or the like, which is generated by the host software application. An example of the 3D point cloud reconstructionis shown in a viewF in. Here, the points combine to make a virtual image of the scene in virtual space. Thus, the original image of the scene (picture) is converted into a virtual image of the scene. This display is in some embodiments part of one or more of a virtual reality experience for a user, an augmented reality for a user, an automated driving experience for a user, etc.

4 FIG.A 4 FIG.A 400 400 401 402 403 illustrates a flow diagram of a method, according to example embodiments. Referring to, the methodmay include executing a neural network on an image of a scene to generate a description of the scene in. The method may include generating a prompt that includes the description of the scene and a request to generate multiple views of the scene in. The method may include executing a large language model (LLM) on the prompt to generate multiple descriptions of the multiple views of the scene, respectively in. The method may include executing a transformer model on the multiple descriptions of the multiple views of the scene and the image of the scene to generate a three-dimensional visual representation of the scene in virtual space in 404.

4 FIG.B 4 FIG.B 410 411 412 illustrates a flow diagram of a method, according to example embodiments. Referring to, in, the neural network may include an image captioning model, and the executing the neural network may include executing the image captioning model on the image of the scene to generate a semantic description of spatial relationships between objects in the scene. In, the neural network may include a contrastive learning model, and the executing the neural network may include connecting the image of the scene to text in an embedding space based on the contrastive learning model and generating the description of the scene based on the text.

413 414 415 416 In, the generating the prompt may include inserting the description of the scene into a prompt template to generate the prompt, where the prompt template comprises a request to generate descriptions of different views of the scene from different perspectives. In, the executing the LLM on the prompt may include executing the LLM on the prompt to generate descriptions of a view from a top of the scene, a view from a bottom of the scene, a view from left of the scene, and a view from right of the scene. In, the executing the transformer model may include executing a two-dimensional view encoder on the multiple descriptions of the multiple views of the scene and the image of the scene to generate encodings. In, the executing the transformer model may further include executing a three-dimensional volume decoder on the encodings to generate the three-dimensional visual representation of the scene in virtual space.

Detailed descriptions of training a machine learning model and executing a machine learning model are further described and depicted herein.

5 FIG.A 500 illustrates an artificial intelligence (AI) network diagramA that supports AI-assisted decision points in a software service executing on a computer. As one example, the AI model being trained in the examples herein may refer to an AI model for any of the tasks performed herein including a neural network, an LLM, an embedding model, a 3D volume transformer, and the like. While the example instant solution shown utilizes a neural network, which is a type of machine learning (ML) model, other branches of AI, such as, but not limited to, computer vision, fuzzy logic, expert systems, deep learning, generative AI, and natural language processing, may be employed in developing the AI model in this instant solution. Further, the AI model included in these examples and features of the instant solution is not limited to particular AI algorithms. Any algorithm or combination of algorithms related to supervised, unsupervised, and reinforcement learning may be employed.

The AI models, ML models, neural networks, and other branches of AI, described and/or depicted herein, build upon the fundamentals of predecessor technologies and form the foundation for all future technological advancements in artificial intelligence. An AI classification system describes the stages of AI progression and advancement. The first classification is known as “reactive machines,” followed by present-day AI classification “limited memory machines” (also known as “artificial narrow intelligence”), then progressing to “theory of mind” (also known as “artificial general intelligence”) and reaching the AI classification “self-aware” (also known as “artificial superintelligence”). Present-day limited memory machines are a growing group of AI models built upon the foundation of their predecessors, reactive machines. Reactive machines emulate human responses to stimuli; however, they are limited in their capabilities as they cannot typically learn from prior experience. Once the AI model's learning abilities emerged, its classification was promoted to limited memory machines. In this present-day classification, AI models learn from large volumes of data, detect patterns, solve problems, generate, and predict data, and the like, while inheriting all the capabilities of reactive machines.

Examples of AI models classified as limited memory machines include, but are not limited to, chatbots, virtual assistants, machine learning, neural networks, deep learning, natural language processing, generative AI models, and any future AI models that are yet to be developed possessing characteristics of limited memory machines.

For example, a neural network is a type of machine learning model that relies on training data to learn associations and connections, improving its accuracy for performing high speed data classifications, clustering, and other analyses of data. Such neural network capabilities are the foundation of deep learning models today as well as becoming the foundational blocks of those yet to be developed.

For example, generative AI models combine limited memory machine technologies, incorporating machine learning and deep learning, forming the foundational building blocks of future AI models. For example, theory of mind is the next progression of AI that may be able to perceive, connect, and react by generating appropriate reactions in response to an entity with which the AI model is interacting; all these theory of mind capabilities relies on the fundamentals of generative AI. Furthermore, in an evolution into the self-aware classification, AI models will be able to understand and evoke emotions in the entities they interact with, as well as possessing their own emotions, beliefs, and needs, all of which rely on generative AI fundamentals of learning from experiences to generate and draw conclusions about itself and its surroundings.

AI models may include, but are not limited to, at least one machine learning model, neural network model, deep learning model, generative AI model, or any combination of models from the branches of AI. AI models are integral and core to future artificial intelligence models. As described herein, AI model refers to present-day AI models and future AI models.

Artificial intelligence systems have been built and trained to perform various tasks in an automated manner. For example, artificial intelligence systems receive and understand verbal and/or written dialogue and function as digital assistants, speech-to-text programs, etc. Other artificial intelligence systems are trained on different types of information to allow the trained system to generate content—such as new works of art based on the styles seen, or new compound ideas based on the history of chemical research.

Foundation models are types of artificial intelligence systems that are trained on a broad set of unlabeled data that can be used for different tasks, with minimal fine-tuning. The unlabeled data includes in some instances imagery and/or language. In response to a short prompt being input into the foundation model, the system generates an output such as an entire essay, or a complex image, based on the parameters that are set forth in the input prompt. The foundation model is able to produce an output that attempts to meet the parameters even if the foundation model was never trained with specific training data that included the exact parameters, e.g., was never trained for that exact argument or to generate an image in that way.

Using self-supervised learning and transfer learning, foundation models can apply information that they have learnt about one situation to another. For example, like a human learns how to drive on one car, for example, and without too much effort, could learn how to drive other types of vehicles such as other cars, a truck, or a bus. The foundation model similarly is used to achieve proficiency in some new area without having to be trained completely from scratch. Foundation models seem to have inherent creativity in performing tasks such as stringing together coherent arguments or create entirely original pieces of art. Foundation models are established in the technology of natural-language processing. One example of how foundation models are helpful is that for previous generation of AI techniques, if you wanted to build an AI model that could summarize bodies of text for you, you would need tens of thousands of labeled examples just for the summarization use case. With a pre-trained foundation model, the labeled data requirements are dramatically reduced. First, the foundation model is fine-tuned with a domain-specific unlabeled corpus to create a domain-specific foundation model. Then, using a much smaller amount of labeled data, potentially just a thousand labeled examples, a foundation model is trained for summarization. The domain-specific foundation model can be used for many tasks as opposed to the previous technologies that required building models from scratch in each use case. Foundation models are even applicable in areas such as computer programming coding analysis, generation, and repair.

Some foundation models are used for sentiment analysis. With pre-trained foundation models, sentiment analysis on a new language can be trained using as little as a few thousand sentences—100 times fewer annotations required than previous models. Reducing labeling requirements will make it much easier for implementation in various technical areas. Systems that execute specific tasks in a single domain are giving way to broad AI that learns more generally and works across domains and problems. Foundation models, trained on large, unlabeled datasets and fine-tuned for an array of applications, are driving this shift.

Large language models (LLMs) are a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks. LLMs have been implemented at different levels to enhance their natural language understanding (NLU) and natural language processing (NLP) capabilities. This advancement of LLMs has occurred alongside advances in machine learning, machine learning models, algorithms, neural networks and the transformer models that provide the architecture for these AI systems.

LLMs are a class of foundation models, which are trained on enormous amounts of data to provide the foundational capabilities needed to drive multiple use cases and applications, as well as resolve a multitude of tasks. This LLM concept is in stark contrast to the idea of building and training domain specific models for each of these use cases individually, which is prohibitive under many criteria (most importantly cost and infrastructure), stifles synergies and can even lead to inferior performance.

LLMs represent a significant breakthrough in NLP and artificial intelligence. LLMs are accessible through interfaces like Open AI's Chat GPT-3 and GPT-4, which have garnered the support of Microsoft. Other examples include Meta's Llama models and Google's bidirectional encoder representations from transformers (BERT/RoBERTa) and PaLM models. IBM has also recently launched its Granite model series on watsonx.ai, which has become the generative AI backbone for other IBM products like watsonx Assistant and watsonx Orchestrate.

In a nutshell, LLMs are designed to understand and generate text like a human, in addition to other forms of content, based on the vast amount of data used to train them. They have the ability to infer from context, generate coherent and contextually relevant responses, translate to languages other than English, summarize text, answer questions (general conversation and FAQs) and even assist in creative writing or code generation tasks. LLMs are able to do some or all of these tasks thanks to many, e.g., billions of, parameters that enable them to capture intricate patterns in language and perform a wide array of language-related tasks. LLMs are revolutionizing applications in various fields, from chatbots and virtual assistants to content generation, research assistance and language translation.

LLMs operate by leveraging deep learning techniques and vast amounts of textual data. These models are typically based on a transformer architecture, like the generative pre-trained transformer, which excels at handling sequential data like text input. LLMs consist of multiple layers of neural networks, each with parameters that can be fine-tuned during training, which are enhanced further by a numerous layer known as the attention mechanism, which dials in on specific parts of data sets.

During the training process, these models learn to predict the next word in a sentence based on the context provided by the preceding words. The model does this through attributing a probability score to the recurrence of words that have been tokenized—broken down into smaller sequences of characters. These tokens are then transformed into embeddings, which are numeric representations of this context.

To ensure accuracy, this process involves training the LLM on a massive corpora of text (e.g., in the billions of pages), allowing the LLM to learn grammar, semantics and conceptual relationships through zero-shot and self-supervised learning. Once trained on this training data, LLMs can generate text by autonomously predicting the next word based on the input they receive, and drawing on the patterns and knowledge they've acquired. The result is coherent and contextually relevant language generation that can be harnessed for a wide range of NLU and content generation tasks.

Model performance can also be increased through prompt engineering, prompt-tuning, fine-tuning and other tactics like reinforcement learning with human feedback (RLHF) to remove the biases, hateful speech and factually incorrect answers known as “hallucinations” that are often unwanted byproducts of training on so much unstructured data. LLMs augment conversational AI in chatbots and virtual assistants (like IBM watsonx Assistant and Google's BARD) to enhance the interactions that provide context-aware responses that mimic interactions with human agents.

LLMs also excel in content generation, automating content creation for blog articles, explanatory materials, and other writing tasks. LLMs aid in summarizing and extracting information from vast datasets, accelerating knowledge discovery. LLMs also play a vital role in language translation, breaking down language barriers by providing accurate and contextually relevant translations. LLMs can even be used to write code, or “translate” between programming languages. LLMs contribute to accessibility by assisting individuals with disabilities, including text-to-speech applications and generating content in accessible formats.

Text generation: language generation abilities, such as writing emails, blog posts or other mid-to-long form content in response to prompts that can be refined and polished. An excellent example is retrieval-augmented generation (RAG). Content summarization: summarize long articles, news stories, research reports, corporate documentation and even interaction history into thorough texts tailored in length to the output format. AI assistants: chatbots that answer queries, perform backend tasks and provide detailed information in natural language as a part of an integrated, self-serve solution for handling inquiries. Code generation: assists developers in building applications, finding errors in code and uncovering security issues in multiple programming languages, even “translating” between them. Sentiment analysis: analyze text to determine a user's tone in order to understand user feedback at scale and aid in brand reputation management. Language translation: provides wider coverage to organizations across languages and geographies with fluent translations and multilingual capabilities. LLMs often include abilities such as:

504 502 520 520 524 504 504 506 5 FIG.A 5 FIG.A 5 FIG.A Software service(see), executing on host platform(see) may provide one or more application programming interfaces (APIs)that enable interaction with other software components via a set of data definitions and protocols. In some examples and features of the instant solution, the APIs provided may employ Simple Object Access Protocol (SOAP), Remote Procedure Calls (RPC), and Representational State Transfer (REST) techniques. In some examples and features of the instant solution, the plurality of APIssend data to one or more decision subsystemsof the software serviceto assist in decision-making. In some examples and features of the instant solution, the software servicestores data included in API requests or data generated during processing the API requests into one or more databases(see).

504 522 522 522 524 504 504 506 Software servicemay provide one or more user interfaces (UIs), such as a server-side hosted graphical user interface (GUI). In some examples and features of the instant solution, the UIsprovided employ template-based frameworks, component-based frameworks, etc. In some examples and features of the instant solution, these UIssend data to one or more decision subsystemsof the software serviceto assist with decision-making. In some examples and features of the instant solution, the software servicestores data included in UI requests or data generated during processing the UI requests into one or more databases.

504 524 504 524 520 524 522 524 506 524 520 522 Software servicemay include one or more decision subsystemsthat drive a decision-making process of the software service. In some examples and features of the instant solution, the decision subsystemsreceive data from one or more APIsas input into the decision-making process. In some examples and features of the instant solution, a decision subsystemmay receive data from one or more UIsas input to the decision-making process. A decision subsystemmay gather service configuration or historical execution data from one or more databasesto aid in the decision-making process. A decision subsystemmay provide feedback to an APIor a UI.

530 524 504 530 532 530 530 530 An AI production systemmay be used by a decision subsystemin a software serviceto assist in its decision-making process. The AI production systemincludes one or more AI modelsthat are executed to generate a response, such as, but not limited to, a prediction, a categorization, a UI prompt, etc. In some examples and features of the instant solution, an AI production systemis hosted on a server. In some examples and features of the instant solution, the AI production systemis cloud-hosted. In some examples and features of the instant solution, the AI production systemis deployed in a distributed multi-node architecture.

540 532 540 550 532 550 540 530 540 540 540 540 An AI development systemcreates one or more AI models. In some examples and features of the instant solution, the AI development systemutilizes data from one or more data sourcesto develop and train one or more AI models. The data sourcesmay be local or third-party data sources. Further, the data provided by the data sources may be real-world or synthetic. In some examples and features of the instant solution, the AI development systemutilizes feedback data from one or more AI production systemsfor new model development and/or existing model re-training. In some examples and features of the instant solution, the AI development systemresides and executes on a server. In some examples and features of the instant solution, the AI development systemis cloud hosted. In some examples and features of the instant solution, the AI development systemis deployed in a distributed multi-node architecture. In some examples and features of the instant solution, the AI development systemutilizes a distributed data pipeline/analytics engine.

532 540 560 540 530 560 560 560 530 560 Once an AI modelhas been trained and validated in the AI development system, it may be stored in an AI model registryfor retrieval by either the AI development systemor by one or more AI production systems. The AI model registryresides in a dedicated server in one example of the instant solution. In some examples and features of the instant solution, the AI model registryis cloud-hosted. In some examples and features of the instant solution, the AI model registryresides in the AI production system. In some examples and features of the instant solution, the AI model registryis a distributed database.

5 FIG.B 500 540 532 541 550 530 illustrates a processB for developing one or more AI models that support AI-assisted decision points. An AI development systemexecutes steps to develop an AI modelthat begins with data extraction, in which data is loaded and ingested from one or more data sources. In some examples and features of the instant solution, historical model feedback data is extracted from one or more AI production systems.

541 542 542 Once the data has been extracted during data extraction, it undergoes data preparationfor model training. In some examples and features of the instant solution, this step involves statistical testing of the data to see how well it reflects real-world events, its distribution, the variety of data in the dataset, etc., and the results of this statistical testing may lead to one or more data transformations being employed to normalize one or more values in the dataset. In some examples and features of the instant solution, data deemed to be noisy is cleaned. A noisy dataset includes values that do not contribute to the training, such as, but not limited to, null and long string values. Data preparationmay be a manual process or an automated process using one or more of the elements and/or functions described and/or depicted herein.

543 542 542 532 532 Features of the data are identified and extracted during the feature extraction step. In some examples and features of the instant solution, a feature of the data is internal to the prepared data from the data preparation step. In some examples and features of the instant solution, a feature of the data requires a piece of prepared data from the data preparation stepto be enriched by data from another data source to be useful in developing the AI model. In some examples and features of the instant solution, identifying relevant features (relevant attributes) for model training are performed via an automated process using one or more of the elements and/or functions described and/or depicted herein. Once the features have been identified, the values of the features are collected into a dataset that will be used to develop the AI model.

543 544 532 532 The dataset output from the feature extraction stepis splitinto a training and validation data set. The training data set is used to train the AI model, and the validation data set is used to evaluate the performance of the AI modelon unseen data.

532 544 532 540 544 The AI modelis trained and tuned 545 using the training data set from the data splitting step. In this step, the training data set is provided to an AI algorithm and an initial set of algorithm parameters which may be automatically determined based on the interdependence between the relevant attributes determined according to various embodiments. The performance of the AI modelis then tested within the AI development systemutilizing the validation data set from step. These steps may be repeated with adjustments to one or more algorithm parameters until the model's performance is acceptable based on various goals and/or results.

532 530 530 544 540 540 532 560 546 The AI modelis evaluated 546 in a staging environment (not shown) that resembles the target AI production system. This evaluation uses a validation dataset to ensure the performance in an AI production systemmatches or exceeds expectations. In some examples and features of the instant solution, the validation dataset from stepis used. In some examples and features of the instant solution, one or more unseen validation datasets are used. In some examples and features of the instant solution, the staging environment is part of the AI development system, and the staging environment is managed separately from the AI development system. Once the AI modelhas been validated, it is stored in an AI model registry, where it can be retrieved for deployment and future updates. In some examples and features of the instant solution, the model evaluation stepmay be a manual process or an automated process using one or more of the elements and/or functions described and/or depicted herein.

541 548 541 548 550 In some examples and features of the instant solution, the AI development system includes a user interface (not shown). The user interface may be used to manage the development system infrastructure, the steps-within the development system, the interim data transmitted between the various steps-, and the data sources.

532 560 547 530 532 540 532 530 548 540 548 532 541 548 550 Once an AI modelhas been validated and published to an AI model registry, it may be deployed during the model deployment stepto one or more AI production systems. In some examples and features of the instant solution, the performance of deployed AI modelis monitored 548 by the AI development system. In some examples and features of the instant solution, AI modelfeedback data is provided by the AI production systemto enable model performance monitoring, and the AI development systemperiodically requests feedback data for model performance monitoring, which includes one or more triggers that result in the AI modelbeing updated by repeating steps-with updated data from one or more data sources.

5 FIG.C 500 illustrates a processC for utilizing an AI model that supports AI-assisted decision points. As stated previously, the AI model utilization process depicted herein reflects ML, which is a particular branch of AI, but this instant solution is not limited to ML and is not limited to any AI algorithm or combination of algorithms.

5 FIG.C 530 524 504 530 534 536 532 520 504 522 504 504 Referring to, an AI production systemmay be used by a decision subsystemin software serviceto assist in its decision-making process. The AI production systemprovides an API, executed by an AI server processthrough which requests can be made. In some examples and features of the instant solution, a request may include an AI modelidentifier to be executed based on the type of request. In some examples and features of the instant solution, a data payload (e.g., to be input to the AI model during execution) is included in the request. The data payload may include APIdata from software service, UIdata from software serviceor data from other software servicesubsystems (not shown).

534 536 532 537 550 536 532 536 524 504 522 504 504 532 538 536 Upon receiving the APIrequest, the AI server processmay transform 537 the data payload or portions of the data payload to be valid feature values in an AI model. Data transformationmay include, but is not limited to, combining data values, normalizing data values, and enriching the incoming data with data from other data sources. Once the data transformation occurs, the AI server processexecutes the appropriate AI modelusing the transformed input data. Upon receiving the execution result, the AI server processresponds to the API requester, which is a decision subsystemof software service. In some examples and features of the instant solution, the response may result in an update to a UIin software service. In some examples and features of the instant solution, the response includes a request identifier that can be used later by the software serviceto provide feedback on the performance of the AI model. In some examples and features of the instant solution, a model feedback record may be added into a model feedback databy the AI server process.

534 532 532 532 534 536 538 538 548 540 540 538 532 In some examples and features of the instant solution, the APIincludes an interface to provide AI modelfeedback after an AI modelexecution response has been processed. This mechanism enables the requester to provide feedback on the accuracy of the AI modelresults. In some examples and features of the instant solution, the feedback interface includes the identifier of the initial request so that it can be used to associate the feedback with the request. Upon receiving a call into the feedback interface of the API, the AI server processcreates and adds a model feedback record into the model feedback datawhich holds historical model feedback records. In some examples and features of the instant solution, the records in this model feedback dataare provided to model performance monitoringin the AI development system. This model feedback data is streamed to the AI development systemor may be provided upon request. In some examples and features of the instant solution, the model feedback records in the model feedback dataare used as an input for retraining the AI model.

530 530 538 In some examples and features of the instant solution, the AI production systemincludes a user interface (not shown). The user interface may be used to manage the production system infrastructure, the components of the production system-, and the operation of the AI production system and its components.

The above embodiments may be implemented in hardware, in a computer program executed by a processor, in firmware, or in a combination of the above. A computer program may be embodied on a computer readable medium, such as a storage medium. For example, a computer program may reside in random access memory (“RAM”), flash memory, read-only memory (“ROM”), erasable programmable read-only memory (“EPROM”), electrically erasable programmable read-only memory (“EEPROM”), registers, hard disk, a removable disk, a compact disk read-only memory (“CD-ROM”), or any other form of storage medium known in the art.

An exemplary storage medium may be coupled to the processor such that the processor may read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application-specific integrated circuit (“ASIC”). In the alternative, the processor and the storage medium may reside as discrete components.